Institute of Electrical and Electronics Engineers (IEEE)

IEEE Transactions on Software Engineering

, volume 49 , issue 7 , pages 3939-3954

Context-Aware Neural Fault Localization

Zhuo Zhang ¹

Yan Lei ¹

XIAOGUANG MAO ²

Meng Yan ¹

Xin Xia ³

David Yung-An Lo ⁴

Hide authors affiliations Show authors affiliations: 4 affiliations

School of Big Data and Software Engineering, Chongqing University, Chongqing, China |

College of Computer, National University of Defense Technology, Changsha, Hunan, China |

College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China |

⁴

School of Information Systems, Singapore Management University, Singapore |

Publication type: Journal Article

Publication date: 2023-07-01

Institute of Electrical and Electronics Engineers (IEEE)

IEEE Transactions on Software Engineering

scimago Q1

wos Q1

SJR: 1.447

CiteScore: 12.9

Impact factor: 5.6

ISSN: 00985589, 19393520, 23263881

DOI: 10.1109/tse.2023.3279125

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Software

Abstract

Numerous fault localization techniques identify suspicious statements potentially responsible for program failures by discovering the statistical correlation between test results (i.e., failing or passing ) and the executions of the different statements of a program (i.e., covered or not covered ). They rarely incorporate a failure context into their suspiciousness evaluation despite the fact that a failure context showing how a failure is produced is useful for analyzing and locating faults. Since a failure context usually contains the transitive relationships among the statements of causing a failure, its relationship complexity becomes one major obstacle for the context incorporation in suspiciousness evaluation of fault localization. To overcome the obstacle, our insight is that leveraging the promising learning ability may be a candidate solution to learn a feasible model for incorporating a failure context into fault localization. Thus, we propose a context-aware neural fault localization approach (CAN). Specifically, CAN represents the failure context by constructing a program dependency graph, which shows how a set of statements interact with each other (i.e., data and control dependencies) to cause a failure. Then, CAN utilizes graph neural networks to analyze and incorporate the context (e.g., the dependencies among the statements) into suspiciousness evaluation. Our empirical results on the 12 large-sized programs show that CAN achieves promising results (e.g., 29.23% faults are ranked within top 5), and it significantly improves the state-of-the-art baselines with a substantial margin.

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GOST |

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GOST Copy

Zhang Z. et al. Context-Aware Neural Fault Localization // IEEE Transactions on Software Engineering. 2023. Vol. 49. No. 7. pp. 3939-3954.

GOST all authors (up to 50) Copy

Zhang Z., Lei Y., MAO X., Yan M., Xia X., Lo D. Y. Context-Aware Neural Fault Localization // IEEE Transactions on Software Engineering. 2023. Vol. 49. No. 7. pp. 3939-3954.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1109/tse.2023.3279125

UR - https://ieeexplore.ieee.org/document/10132088/

TI - Context-Aware Neural Fault Localization

T2 - IEEE Transactions on Software Engineering

AU - Zhang, Zhuo

AU - Lei, Yan

AU - MAO, XIAOGUANG

AU - Yan, Meng

AU - Xia, Xin

AU - Lo, David Yung-An

PY - 2023

DA - 2023/07/01

PB - Institute of Electrical and Electronics Engineers (IEEE)

SP - 3939-3954

IS - 7

VL - 49

SN - 0098-5589

SN - 1939-3520

SN - 2326-3881

ER -

BibTex |

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BibTex (up to 50 authors) Copy

@article{2023_Zhang,

author = {Zhuo Zhang and Yan Lei and XIAOGUANG MAO and Meng Yan and Xin Xia and David Yung-An Lo},

title = {Context-Aware Neural Fault Localization},

journal = {IEEE Transactions on Software Engineering},

year = {2023},

volume = {49},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

month = {jul},

url = {https://ieeexplore.ieee.org/document/10132088/},

number = {7},

pages = {3939--3954},

doi = {10.1109/tse.2023.3279125}

}

MLA

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MLA Copy

Zhang, Zhuo, et al. “Context-Aware Neural Fault Localization.” IEEE Transactions on Software Engineering, vol. 49, no. 7, Jul. 2023, pp. 3939-3954. https://ieeexplore.ieee.org/document/10132088/.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Journal

IEEE Transactions on Software Engineering

scimago Q1

wos Q1

SJR

1.447

CiteScore

12.9

Impact factor

5.6

ISSN

00985589 (Print)

19393520 (Electronic)

23263881 (Electronic)